Many digital imaging systems are moving towards multi-channel image processing as a means to increase throughput. For example, many CMOS image sensors will readout 4 channels of pixel data in parallel, achieving a 120 Mega pixels (Mpixel)/sec throughput, while each readout channel speed is only 30 Mpixel/sec.
While breaking the pixel processing into parallel paths allows improved throughput, it also can impair system performance. One such impairment involves mis-match among channels. In a multi-channel system, equivalent input to each channel should generate equivalent outputs from each channel. If the output data is not uniform in response to uniform inputs, the digitized image would not be an accurate representation of the captured light. Mis-match among the outputs could give rise to perceptual artifacts.
In digital imaging systems, the transfer function from input (light) to output (digital data) is often quite non-linear. Non-linear performance can be accommodated because the sensitivity of the human eye is also non-linear. In fact, this allows the system designer extra freedom, such as allowing for non-linear analog front-ends (AFEs), which can save power and cost as compared to highly linear AFEs. In multi-channel systems, it is often difficult to design non-linear AFEs that match well because non-linearity behavior is generally not well controlled. One AFE might be non-linear in one way, and the next could be non-linear in a different way. Thus, the matching between channels suffers and can lead to artifacts. Accordingly, there is a need for matching the outputs of channels of a multi-channel imaging systems.